Evidence map›Paper›PMID 41546706›Full record

ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2026

Characterization of the genetic architecture of adult plant resistance to sharp eyespot in Chinese wheat germplasm.

Caiyun Liu, Wenling Zhai, Ningning Kang, Yanjiang He, Bisheng Fu, Wei Guo, Qiaofeng Zhang, Jin Cai, Ying Liu, Guang Qiu and 5 more

Abstract read
In one paragraph

Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Journal of fungi (Basel, Switzerland) · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Caiyun Liu *Institute of Germplasm Resources and Biotechnology/Jiangsu Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
Wenling Zhai *Institute of Germplasm Resources and Biotechnology/Jiangsu Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
Ningning KangInstitute of Germplasm Resources and Biotechnology/Jiangsu Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
Yanjiang HeInstitute of Germplasm Resources and Biotechnology/Jiangsu Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
Bisheng FuInstitute of Germplasm Resources and Biotechnology/Jiangsu Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
Wei GuoInstitute of Germplasm Resources and Biotechnology/Jiangsu Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
Qiaofeng ZhangInstitute of Germplasm Resources and Biotechnology/Jiangsu Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
Jin CaiInstitute of Germplasm Resources and Biotechnology/Jiangsu Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
Ying LiuInstitute of Germplasm Resources and Biotechnology/Jiangsu Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
Guang QiuInstitute of Germplasm Resources and Biotechnology/Jiangsu Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
Hua JiangInstitute of Germplasm Resources and Biotechnology/Jiangsu Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
Fujie LiuInstitute of Germplasm Resources and Biotechnology/Jiangsu Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China.
Feng XuCollege of Agriculture, Anhui Science and Technology University, Fengyang, 233100, Anhui, China.
Huagang HeSchool of Life Sciences, Jiangsu University, Zhenjiang, 212013, China.
Jizhong WuInstitute of Germplasm Resources and Biotechnology/Jiangsu Provincial Key Laboratory of Agrobiology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, Jiangsu, China. wujz@jaas.ac.cn.ORCID http://orcid.org/0000-0002-0860-6504

Funding

Jiangsu Seed Industry Revitalization Project JBGS (2021) 013National Key Research and Development Program of China 2024YFD1201102National Natural Science Foundation of China 32101703National Natural Science Foundation of China 32572322Zhongshan Biological Breeding Laboratory ZSBBL-KY2023-02-1
6 · The paper itself

Abstract

key messageFour QTLs associated with sharp eyespot resistance in wheat were identified through genome-wide association study, and genomic prediction achieved high accuracy, providing valuable tools for practice in sharp eyespot resistance breeding. Sharp eyespot, caused by the soilborne fungus Rhizoctonia cerealis, is becoming a major disease affecting wheat production in China. To improve resistance to sharp eyespot in wheat, a genome-wide association study (GWAS) was conducted to uncover the genetic architecture underlying adult plant resistance, and genomic selection (GS) was evaluated for its potential to accelerate breeding. The disease index of sharp eyespot evaluated in 427 Chinese wheat accessions across five environments demonstrated a broad range of phenotypic variation and exhibited significant correlations among the environments. Disease index was negatively correlated with phenology but positively correlated with plant height. Thirty-five accessions (8.2%) showed stable moderate resistance across the environments. GWAS using 159,248 markers identified 35 significant SNPs associated with sharp eyespot resistance. These SNPs delineated four quantitative trait loci (QTLs): QSe.jaas-1B (583-586 Mb), QSe.jaas-5D (464-470 Mb), QSe.jaas-6B (191-207 Mb), and QSe.jaas-6D (385 Mb). QTL pyramiding demonstrated additive effects, and combining four favorable QTLs reduced disease index by 22%. GS using only 35 significant SNPs retained over 90% of the predictive ability of the full-genome marker set, with the Bayes B model (accuracy: 0.52-0.57) outperforming GBLUP (0.46-0.50). These findings provide key genomic resources, including stable resistant germplasm, QTLs with favorable haplotypes, and predictive models, which will be valuable for improving sharp eyespot resistance in wheat breeding programs.

Indexed as

Disease ResistanceRhizoctoniaTriticumBasidiomycotaChinaGenetic MarkersHaplotypesHost-Pathogen InteractionsPlant DiseasesPolymorphism, Single NucleotidePredictive Learning ModelsQuantitative Trait LociGenetic Markers

Identifiers

PMID41546706
PMCPMC12812090

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.